test: included new benchmark tests

core/flploc_test.go

@@ -3,6 +3,7 @@ package pigo_test
 import (
 	"io/ioutil"
 	"log"
+	"runtime"
 	"testing"
 
 	pigo "github.com/esimov/pigo/core"
@@ -159,7 +160,37 @@ func TestFlploc_LandmarkDetectorShouldReturnCorrectDetectionPoints(t *testing.T)
 	}
 }
 
-func BenchmarkFlploc(b *testing.B) {
+func BenchmarkFlplocReadCascadeDir(b *testing.B) {
+	for i := 0; i < b.N; i++ {
+		plc.ReadCascadeDir("../cascade/lps/")
+	}
+}
+
+func BenchmarkFlplocGetLendmarkPoint(b *testing.B) {
+	pl := pigo.PuplocCascade{}
+	plc, err := pl.UnpackCascade(puplocCascade)
+	if err != nil {
+		b.Fatalf("error reading the cascade file: %s", err)
+	}
+
+	pixs := pigo.RgbToGrayscale(srcImg)
+	cParams.Pixels = pixs
+
+	flploc := &pigo.Puploc{Row: 10, Col: 10, Scale: 20, Perturbs: 50}
+	// For benchmarking we are using common values for left and right eye.
+	puploc := plc.RunDetector(*flploc, *imgParams, 0.0, false)
+
+	b.ResetTimer()
+	runtime.GC()
+
+	for i := 0; i < b.N; i++ {
+		plc.GetLandmarkPoint(puploc, puploc, *imgParams, 63, false)
+	}
+}
+
+func BenchmarkFlplocDetection(b *testing.B) {
+	var faces []pigo.Detection
+
 	pg := pigo.NewPigo()
 	// Unpack the binary file. This will return the number of cascade trees,
 	// the tree depth, the threshold and the prediction from tree's leaf nodes.
@@ -174,19 +205,19 @@ func BenchmarkFlploc(b *testing.B) {
 		b.Fatalf("error reading the cascade file: %s", err)
 	}
 
-	var faces []pigo.Detection
+	pixs := pigo.RgbToGrayscale(srcImg)
+	cParams.Pixels = pixs
 
 	b.ResetTimer()
+	runtime.GC()
+
+	// Run the classifier over the obtained leaf nodes and return the detection results.
+	// The result contains quadruplets representing the row, column, scale and detection score.
+	faces = classifier.RunCascade(*cParams, 0.0)
+	// Calculate the intersection over union (IoU) of two clusters.
+	faces = classifier.ClusterDetections(faces, 0.1)
 
 	for i := 0; i < b.N; i++ {
-		pixs := pigo.RgbToGrayscale(srcImg)
-		cParams.Pixels = pixs
-		// Run the classifier over the obtained leaf nodes and return the detection results.
-		// The result contains quadruplets representing the row, column, scale and detection score.
-		faces = classifier.RunCascade(*cParams, 0.0)
-		// Calculate the intersection over union (IoU) of two clusters.
-		faces = classifier.ClusterDetections(faces, 0.1)
-
 		for _, face := range faces {
 			if face.Scale > 50 {
 				// left eye

core/pigo_test.go

@@ -5,6 +5,7 @@ import (
 	"io/ioutil"
 	"log"
 	"path/filepath"
+	"runtime"
 	"testing"
 
 	pigo "github.com/esimov/pigo/core"
@@ -68,43 +69,82 @@ func TestPigo_InputImageShouldBeGrayscale(t *testing.T) {
 }
 
 func TestPigo_Detector_ShouldDetectFace(t *testing.T) {
-	// Unpack the binary file. This will return the number of cascade trees,
+	// Unpack the facefinder binary cascade file. This will return the number of cascade trees,
 	// the tree depth, the threshold and the prediction from tree's leaf nodes.
-	classifier, err := p.Unpack(pigoCascade)
+	p, err := p.Unpack(pigoCascade)
 	if err != nil {
 		t.Fatalf("error reading the cascade file: %s", err)
 	}
 
 	// Run the classifier over the obtained leaf nodes and return the detection results.
 	// The result contains quadruplets representing the row, column, scale and detection score.
-	faces := classifier.RunCascade(*cParams, 0.0)
+	faces := p.RunCascade(*cParams, 0.0)
 	// Calculate the intersection over union (IoU) of two clusters.
-	faces = classifier.ClusterDetections(faces, 0.1)
+	faces = p.ClusterDetections(faces, 0.1)
 	if len(faces) == 0 {
 		t.Fatalf("face should've been detected")
 	}
 }
 
-func BenchmarkPigo(b *testing.B) {
+func BenchmarkPigoUnpackCascade(b *testing.B) {
 	pg := pigo.NewPigo()
-	// Unpack the binary file. This will return the number of cascade trees,
-	// the tree depth, the threshold and the prediction from tree's leaf nodes.
-	classifier, err := pg.Unpack(pigoCascade)
-	if err != nil {
-		log.Fatalf("Error reading the cascade file: %s", err)
-	}
-
-	var dets []pigo.Detection
-	b.ResetTimer()
 
 	for i := 0; i < b.N; i++ {
-		pixs := pigo.RgbToGrayscale(srcImg)
+		// Unpack the facefinder binary cascade file.
-		cParams.Pixels = pixs
+		_, err := pg.Unpack(pigoCascade)
+		if err != nil {
+			log.Fatalf("error reading the cascade file: %s", err)
+		}
+	}
+}
+
+func BenchmarkPigoFaceDetection(b *testing.B) {
+	var dets []pigo.Detection
+
+	pg := pigo.NewPigo()
+	p, err := pg.Unpack(pigoCascade)
+	if err != nil {
+		log.Fatalf("error reading the cascade file: %s", err)
+	}
+
+	pixs := pigo.RgbToGrayscale(srcImg)
+	cParams.Pixels = pixs
+
+	b.ResetTimer()
+	runtime.GC()
+
+	for i := 0; i < b.N; i++ {
 		// Run the classifier over the obtained leaf nodes and return the detection results.
 		// The result contains quadruplets representing the row, column, scale and detection score.
-		dets = classifier.RunCascade(*cParams, 0.0)
+		dets = p.RunCascade(*cParams, 0.0)
 		// Calculate the intersection over union (IoU) of two clusters.
-		dets = classifier.ClusterDetections(dets, 0.1)
+		dets = p.ClusterDetections(dets, 0.1)
+	}
+	_ = dets
+}
+
+func BenchmarkPigoClusterDetection(b *testing.B) {
+	var dets []pigo.Detection
+
+	pg := pigo.NewPigo()
+	p, err := pg.Unpack(pigoCascade)
+	if err != nil {
+		log.Fatalf("error reading the cascade file: %s", err)
+	}
+
+	pixs := pigo.RgbToGrayscale(srcImg)
+	cParams.Pixels = pixs
+
+	b.ResetTimer()
+	runtime.GC()
+
+	// Run the classifier over the obtained leaf nodes and return the detection results.
+	// The result contains quadruplets representing the row, column, scale and detection score.
+	dets = p.RunCascade(*cParams, 0.0)
+
+	for i := 0; i < b.N; i++ {
+		// Calculate the intersection over union (IoU) of two clusters.
+		dets = p.ClusterDetections(dets, 0.1)
 	}
 	_ = dets
 }

core/puploc_test.go

@@ -3,6 +3,7 @@ package pigo_test
 import (
 	"io/ioutil"
 	"log"
+	"runtime"
 	"testing"
 
 	pigo "github.com/esimov/pigo/core"
@@ -36,18 +37,18 @@ func TestPuploc_UnpackCascadeFileShouldNotBeNil(t *testing.T) {
 
 func TestPuploc_Detector_ShouldDetectEyes(t *testing.T) {
 	p := pigo.NewPigo()
-	// Unpack the binary file. This will return the number of cascade trees,
+	// Unpack the facefinder binary cascade file. This will return the number of cascade trees,
 	// the tree depth, the threshold and the prediction from tree's leaf nodes.
-	classifier, err := p.Unpack(pigoCascade)
+	p, err := p.Unpack(pigoCascade)
 	if err != nil {
 		t.Fatalf("error reading the cascade file: %s", err)
 	}
 
 	// Run the classifier over the obtained leaf nodes and return the detection results.
 	// The result contains quadruplets representing the row, column, scale and detection score.
-	faces := classifier.RunCascade(*cParams, 0.0)
+	faces := p.RunCascade(*cParams, 0.0)
 	// Calculate the intersection over union (IoU) of two clusters.
-	faces = classifier.ClusterDetections(faces, 0.1)
+	faces = p.ClusterDetections(faces, 0.1)
 
 	eyes := []pigo.Puploc{}
 
@@ -79,11 +80,50 @@ func TestPuploc_Detector_ShouldDetectEyes(t *testing.T) {
 	}
 }
 
-func BenchmarkPuploc(b *testing.B) {
+func BenchmarkPuplocUnpackCascade(b *testing.B) {
 	pg := pigo.NewPigo()
-	// Unpack the binary file. This will return the number of cascade trees,
+
-	// the tree depth, the threshold and the prediction from tree's leaf nodes.
+	// Unpack the facefinder binary cascade file.
-	classifier, err := pg.Unpack(pigoCascade)
+	_, err := pg.Unpack(pigoCascade)
+	if err != nil {
+		log.Fatalf("error reading the cascade file: %s", err)
+	}
+
+	b.ResetTimer()
+	runtime.GC()
+
+	for i := 0; i < b.N; i++ {
+		pl := pigo.PuplocCascade{}
+		// Unpack the pupil localization cascade file.
+		_, err = pl.UnpackCascade(puplocCascade)
+		if err != nil {
+			b.Fatalf("error reading the cascade file: %s", err)
+		}
+	}
+}
+
+func BenchmarkPuplocDetectorRun(b *testing.B) {
+	pl := pigo.PuplocCascade{}
+
+	plc, err := pl.UnpackCascade(puplocCascade)
+	if err != nil {
+		b.Fatalf("error reading the cascade file: %s", err)
+	}
+
+	pixs := pigo.RgbToGrayscale(srcImg)
+	cParams.Pixels = pixs
+
+	puploc := &pigo.Puploc{Row: 10, Col: 10, Scale: 20, Perturbs: 50}
+	for i := 0; i < b.N; i++ {
+		plc.RunDetector(*puploc, *imgParams, 0.0, false)
+	}
+}
+
+func BenchmarkPuplocDetection(b *testing.B) {
+	var faces []pigo.Detection
+
+	pg := pigo.NewPigo()
+	p, err := pg.Unpack(pigoCascade)
 	if err != nil {
 		b.Fatalf("error reading the cascade file: %s", err)
 	}
@@ -94,19 +134,19 @@ func BenchmarkPuploc(b *testing.B) {
 		b.Fatalf("error reading the cascade file: %s", err)
 	}
 
-	var faces []pigo.Detection
+	pixs := pigo.RgbToGrayscale(srcImg)
+	cParams.Pixels = pixs
 
 	b.ResetTimer()
+	runtime.GC()
+
+	// Run the classifier over the obtained leaf nodes and return the detection results.
+	// The result contains quadruplets representing the row, column, scale and detection score.
+	faces = p.RunCascade(*cParams, 0.0)
+	// Calculate the intersection over union (IoU) of two clusters.
+	faces = p.ClusterDetections(faces, 0.1)
 
 	for i := 0; i < b.N; i++ {
-		pixs := pigo.RgbToGrayscale(srcImg)
-		cParams.Pixels = pixs
-		// Run the classifier over the obtained leaf nodes and return the detection results.
-		// The result contains quadruplets representing the row, column, scale and detection score.
-		faces = classifier.RunCascade(*cParams, 0.0)
-		// Calculate the intersection over union (IoU) of two clusters.
-		faces = classifier.ClusterDetections(faces, 0.1)
-
 		for _, face := range faces {
 			if face.Scale > 50 {
 				// left eye